A new 3D descriptor for human classification: application for human detection in a multi-kinect system

Kyis Essmaeel 1 Cyrille Migniot 1 Albert Dipanda 1 Luigi Gallo 2 Ernesto Damiani 3 Giuseppe de Pietro 2
1 CORES - Equipe CORES [ImViA - EA7535]
ImViA - Imagerie et Vision Artificielle [Dijon]
2 ICAR-CNR - Istituto di Calcolo e Reti ad Alte Prestazioni [Napoli]
3 Dipartimento di Scienze dell'Informazione [Milano]
Abstract : In this paper we present a new 3D descriptor for human classification and a human detection method based on this descriptor. The proposed 3D descriptor allows classification of an object represented by a point cloud, as human or non-human. It is derived from the well-known Histogram of Oriented Gradient by employing surface normals instead of gradients. The process consists in an appropriate subdivision of the object point cloud into blocks. These blocks provide the spatial distribution modeling of the surface normal orientation into the different parts of the object. This distribution modelling is expressed as a histogram. In addition we have set up a multi-kinect acquisition system that provides us with Complete Point Clouds (CPC) (i.e. 360° view). Such CPCs enable a suitable processing, particularly in case of occlusions. Moreover they allow for the determination of the human frontal orientation. Based on the proposed 3D descriptor, we have developed a human detection method that is applied on CPCs. First, we evaluated the 3D descriptor over a set of CPC candidates by using the Support Vector Machine (SVM) classifier. The learning process was conducted with the original CPC database that we have built. The results are very promising. The descriptor can discriminate human from non-human candidates and provides the frontal direction of humans with high precision. In addition we demonstrated that using the CPCs improves significantly the classification results in comparison with Single Point Clouds (i.e. points clouds acquired with only one kinect). Second, we compared our detection method with two others, namely the HOG detector on RGB images and a 3D HOG-based detection method that is applied on RGB-depth data. The obtained results on different situations show that the proposed human detection method provides excellent performances that outperform the other two detection methods.
Keywords : Human classification 3D descriptor Multi-kinect
Type de document :
Article dans une revue
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https://hal-univ-bourgogne.archives-ouvertes.fr/hal-02125322
Contributeur : Imvia - Université de Bourgogne <>
Soumis le : vendredi 10 mai 2019 - 13:04:07
Dernière modification le : mercredi 22 janvier 2020 - 15:44:03

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UNIV-BOURGOGNE | IMVIA | CORES

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Kyis Essmaeel, Cyrille Migniot, Albert Dipanda, Luigi Gallo, Ernesto Damiani, et al.. A new 3D descriptor for human classification: application for human detection in a multi-kinect system. Multimedia Tools and Applications, Springer Verlag, 2019, 78 (8), pp.1-30. ⟨10.1007/s11042-019-7568-6⟩. ⟨hal-02125322⟩

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